The clash between privacy and utility has been brought to attention and widely studied in the literature [1]}, [2]}. Optimization techniques for differential privacy and utility in the context of statistical databases have been intensively analyzed from various perspectives and have been investigated by the community in the recent past [3]}, [4]}, [5]}. There has been some recent work focusing on devising privacy mechanisms that are optimal to limit the privacy risk against Bayesian inference attacks while maximizing the utility [6]}, [7]}. In [8]} Oya et al. examine a two-way optimal location-privacy preserving mechanism and analyse it with respect to different privacy and utility metrics.
